Image recognition models can provide a powerful tool for quickly identifying objects depicted in images, and processes for training and employing such image recognition have improved greatly in recent years. Improvements in computers allow vast quantities of images to be processed to train such models. However, more is not always better when it comes to such training.
Existing methods for training image recognition models may focus on incorporating as many images as possible, which may improve accuracy for some targets of identification, while doing little to improve accuracy for others. For example, an image recognition model for identifying vehicles may become very well trained to identify popular models of vehicles, while becoming insufficiently trained at identifying less popular models.
A focus strictly on quantity of source images may result in vast quantities of computer resources devoted to training the image recognition model as to popular models of vehicles, while incorporating too few images of less popular models to accurately identify them.
A need therefore exists for systems and methods for improved training of image recognition models.